Both visualization methods discussed above are essentially qualitative. In contrast, Bau  [1]} proposed a quantitative method to give each activation map a semantic meaning.
In their work, they proposed a dataset with 6 image categories and 63,305 images for network dissection, where each image is labeled with pixel-wise semantic meaning. At first, they forward all images in the dataset into a pre-trained deep model. For each activation map inside the model, different inputs have different patterns. Then, they compute the distribution of each unit activation map over the whole dataset, and determine a threshold for each unit based on its activation distribution. With the threshold for each unit, the activation map for each input image is quantized to a binary map. Finally, they compute the intersection over union (IOU) between the quantized activation map and the labeled ground truth to determine what objects or object parts a unit is detecting.
